Implementation Aspects of Graph Neural Networks

Aleksy Barcz , Zbigniew Szymański , Stanisław Jankowski

Abstract

This article summarises the results of implementation of a Graph Neural Network classifier. The Graph Neural Network model is a connectionist model, capable of processing various types of structured data, including non- positional and cyclic graphs. In order to operate correctly, the GNN model must implement a transition function being a contraction map, which is assured by imposing a penalty on model weights. This article presents research results concerning the impact of the penalty parameter on the model training process and the practical decisions that were made during the GNN implementation process.
Author Aleksy Barcz (FEIT / IN)
Aleksy Barcz,,
- The Institute of Computer Science
, Zbigniew Szymański (FEIT / IN)
Zbigniew Szymański,,
- The Institute of Computer Science
, Stanisław Jankowski (FEIT / PE)
Stanisław Jankowski,,
- The Institute of Electronic Systems
Pages89032S-1-89032S-9
Book Romaniuk Ryszard (eds.): Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2013, vol. 8903, 2013, SPIE P.O. Box 10, Bellingham, Washington 98227-0010 USA , SPIE, ISBN 9780819497857, [ISSN 0277-786X ], 410 p., DOI:10.1117/12.2049644
Keywords in EnglishGraph Neural Network, GNN, graph, classification, contraction map
DOIDOI:10.1117/12.2035443
ProjectDevelopment of new methods and algorithms in the following areas: computer graphics, artificial intelligence, and information systems, and distributed systems . Project leader: Rybiński Henryk, , Phone: +48 22 234 7731, start date 29-05-2013, planned end date 31-12-2013, end date 30-11-2014, II/2013/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 10.0, 29-08-2020, BookChapterMatConfByIndicator
Ministerial score (2013-2016) = 15.0, 29-08-2020, BookChapterMatConfByIndicator
Publication indicators Scopus Citations = 0; WoS Citations = 0
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